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A semantic proteomics dashboard (SemPoD) for data management in translational research

Overview of attention for article published in BMC Systems Biology, December 2012
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Title
A semantic proteomics dashboard (SemPoD) for data management in translational research
Published in
BMC Systems Biology, December 2012
DOI 10.1186/1752-0509-6-s3-s20
Pubmed ID
Authors

Catherine P Jayapandian, Meng Zhao, Rob M Ewing, Guo-Qiang Zhang, Satya S Sahoo

Abstract

One of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of 'omics data with clinical information to complete the cycle from the bench to the bedside. The role of contextual metadata, also called provenance information, is a key factor ineffective data integration, reproducibility of results, correct attribution of original source, and answering research queries involving "What", "Where", "When", "Which", "Who", "How", and "Why" (also known as the W7 model). But, at present there is limited or no effective approach to managing and leveraging provenance information for integrating data across studies or projects. Hence, there is an urgent need for a paradigm shift in creating a "provenance-aware" informatics platform to address this challenge. We introduce an ontology-driven, intuitive Semantic Proteomics Dashboard (SemPoD) that uses provenance together with domain information (semantic provenance) to enable researchers to query, compare, and correlate different types of data across multiple projects, and allow integration with legacy data to support their ongoing research.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Thailand 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 16%
Student > Ph. D. Student 9 16%
Researcher 8 14%
Librarian 7 12%
Student > Doctoral Student 5 9%
Other 14 24%
Unknown 6 10%
Readers by discipline Count As %
Computer Science 14 24%
Social Sciences 10 17%
Agricultural and Biological Sciences 9 16%
Medicine and Dentistry 8 14%
Engineering 4 7%
Other 5 9%
Unknown 8 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 January 2013.
All research outputs
#18,326,065
of 22,693,205 outputs
Outputs from BMC Systems Biology
#834
of 1,142 outputs
Outputs of similar age
#203,088
of 261,299 outputs
Outputs of similar age from BMC Systems Biology
#41
of 56 outputs
Altmetric has tracked 22,693,205 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.